# Neural and computational mechanisms of motivation and cognitive control

> **NIH NIH R01** · BROWN UNIVERSITY · 2021 · $397,084

## Abstract

PROJECT SUMMARY/ABSTRACT
Most daily tasks demand cognitive control, but people vary in their motivation to meet the control demands
required of those tasks. Motivational impairments are a common and transdiagnostic feature of a wide range of
psychiatric and neurological disorders—including major depression, schizophrenia, and Alzheimer’s—severely
compromising the daily functioning and overall wellbeing of individuals with these disorders. Unfortunately, little
is known about the neurocomputational mechanisms that drive these impairments. We recently developed a
computational model of how people make decisions about control allocation based on an evaluation of the costs
and benefits (the Expected Value of Control [EVC] model). Our model points to several potential sources of
motivational impairments and their putative neural substrates. These include deficits in learning about incentives,
signaling those incentives when expected, and/or properly utilizing those incentives when making decisions
about control allocation. The model suggests that dorsal anterior cingulate (dACC) is responsible for integrating
incentive information in order to motivate the level of cognitive control that is most worthwhile. Our model further
points to two dissociable components of the incentives for control: (1) the expected efficacy of control (the extent
to which control is necessary to reach a particular goal) and (2) the expected reward for reaching that goal.
Previous research has primarily focused on the latter component. It is therefore largely unknown how efficacy is
learned and anticipated; how it is integrated with reward to guide control allocation; and to what extent
motivational impairments are caused by deficits in the processing of efficacy. We have developed and validated
a set of tasks that tease apart the independent influences of reward and efficacy on effort allocation. We will
have adult participants perform these tasks while undergoing EEG or fMRI, to characterize the
neurocomputational mechanisms by which expected reward and efficacy are (1) signaled, (2) utilized to
determine effort allocation, (3) updated based on feedback, and (4) generalized to novel stimuli. We predict that
dACC will integrate reward and efficacy information from separate frontoparietal inputs, to determine the amount
and type of control that is most worthwhile. This control allocation will be enacted through dACC’s interactions
with goal-specific prefrontal and subcortical regions. We also predict that reward- and efficacy-selective regions
of frontostriatal and frontoparietal circuits will interact to guide learning and generalization of task incentives. We
will test these predictions with model-based analyses of behavior and neural activity, using our EVC model to
generate participant-specific estimates of incentive processing and control allocation across trials. This research
will offer critical new insight into the computations and circuits underlying the motivation of...

## Key facts

- **NIH application ID:** 10099323
- **Project number:** 1R01MH124849-01
- **Recipient organization:** BROWN UNIVERSITY
- **Principal Investigator:** AMITAI SHENHAV
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $397,084
- **Award type:** 1
- **Project period:** 2021-03-02 → 2025-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10099323

## Citation

> US National Institutes of Health, RePORTER application 10099323, Neural and computational mechanisms of motivation and cognitive control (1R01MH124849-01). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10099323. Licensed CC0.

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